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Adaptive random-based self-organizing background subtraction for moving detection

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Abstract

The adaptability plays a significant role in moving detection. The diverse scenarios in real world still challenge this problem. Therefore, in this paper, we proposed an adaptive moving detection method, namely Adaptive Random-based Self-Organizing back- ground subtraction (ABSOBS) method. This method can adaptively extract the moving objects in various conditions and eliminate the “ghost” pixels simultaneously. Therefore, a robust initialization strategy is proposed to remove the noise pixels caused by the initialized frames. The proposed method uses a random- based scheme which allows the foreground pixels to up- date the neural network with a small probability. This strategy allows our algorithm to efficiently handle scene changes. Moreover, a foreground filter based on random rule is designed to eliminate the “ghost” pixel. More importantly, ABSOBS adopts a regulator to control the updating rate in different conditions. It makes our method easy-to-used and need not to set the parameters manually. The experiment results on various scenarios show that our method improves the detection accuracy for the SOBS and outperforms other state-of- the-art methods.

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Acknowledgements

The paper would express sincere appreciation to the from Beijing education science Project (No. SM201810038006); Project supported by Key Teachers for Capital University of economics and business.

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Authors and Affiliations

  1. Information College, Capital University of Economics and Business, Beijing, 100070, China

    Shan Lu

  2. School of Computer Science, Heilongjiang International University, Harbin, 150025, China

    Xianmin Ma

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  1. Shan Lu
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  2. Xianmin Ma
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Correspondence to Xianmin Ma.

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Lu, S., Ma, X. Adaptive random-based self-organizing background subtraction for moving detection. Int. J. Mach. Learn. & Cyber. 11, 1267–1276 (2020). https://doi.org/10.1007/s13042-019-01037-x

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  • DOI: https://doi.org/10.1007/s13042-019-01037-x

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